Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.


Step 0: Load The Data

In [1]:
# Load pickled data
import pickle
import csv

# TODO: Fill this in based on where you saved the training and testing data
signlabels = []

#Select workbook
with open('signnames.csv', 'r') as csvfile:
    readCSV = csv.reader(csvfile, delimiter=',')
    for row in readCSV:
        signlabels += [row[1]]



training_file = "train.p"
validation_file="valid.p"
testing_file = "test.p"

with open(training_file, mode='rb') as f:
    train = pickle.load(f)
with open(validation_file, mode='rb') as f:
    valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
    test = pickle.load(f)
    
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
In [2]:
print()
print("Image Shape: {}".format(X_train[0].shape))
print()
print("Training Set:   {} samples".format(len(X_train)))
print("Validation Set: {} samples".format(len(X_valid)))
print("Test Set:       {} samples".format(len(X_test)))
print(signlabels)
Image Shape: (32, 32, 3)

Training Set:   34799 samples
Validation Set: 4410 samples
Test Set:       12630 samples
['SignName', 'Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'No passing', 'No passing for vehicles over 3.5 metric tons', 'Right-of-way at the next intersection', 'Priority road', 'Yield', 'Stop', 'No vehicles', 'Vehicles over 3.5 metric tons prohibited', 'No entry', 'General caution', 'Dangerous curve to the left', 'Dangerous curve to the right', 'Double curve', 'Bumpy road', 'Slippery road', 'Road narrows on the right', 'Road work', 'Traffic signals', 'Pedestrians', 'Children crossing', 'Bicycles crossing', 'Beware of ice/snow', 'Wild animals crossing', 'End of all speed and passing limits', 'Turn right ahead', 'Turn left ahead', 'Ahead only', 'Go straight or right', 'Go straight or left', 'Keep right', 'Keep left', 'Roundabout mandatory', 'End of no passing', 'End of no passing by vehicles over 3.5 metric tons']

Step 1: Dataset Summary & Exploration

The pickled data is a dictionary with 4 key/value pairs:

  • 'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
  • 'labels' is a 1D array containing the label/class id of the traffic sign. The file signnames.csv contains id -> name mappings for each id.
  • 'sizes' is a list containing tuples, (width, height) representing the original width and height the image.
  • 'coords' is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES

Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.

Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas

In [3]:
import numpy as np

### Replace each question mark with the appropriate value. 
### Use python, pandas or numpy methods rather than hard coding the results

# TODO: Number of training examples
n_train = len(X_train)

# TODO: Number of validation examples
n_validation = len(X_valid)

# TODO: Number of testing examples.
n_test = len(X_test)

# TODO: What's the shape of an traffic sign image?
image_shape = X_train[0].shape

# TODO: How many unique classes/labels there are in the dataset.
labels = np.unique(y_train)
n_classes = len(labels)

print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
Number of training examples = 34799
Number of testing examples = 12630
Image data shape = (32, 32, 3)
Number of classes = 43

Include an exploratory visualization of the dataset

Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?

In [4]:
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.

# Print the histogram of the input data

import cv2 as cv
import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

classes, counts = np.unique(y_train, return_counts=True)

fig = plt.figure(figsize=(10,10))

plt.barh(classes,counts)
#plt.yticks(counts, signlabels)
plt.ylabel('Classes')
plt.xlabel('Frequency')
plt.title('Histogram of the classes')

plt.show()
print(counts)
[ 180 1980 2010 1260 1770 1650  360 1290 1260 1320 1800 1170 1890 1920  690
  540  360  990 1080  180  300  270  330  450  240 1350  540  210  480  240
  390  690  210  599  360 1080  330  180 1860  270  300  210  210]
In [5]:
#display images

### Show images with it label.


plt.figure(figsize=(20, 40))

for i in range(0, n_classes):
    plt.subplot(25, 4, i+1)
    x_start = X_train[y_train == i]
    plt.imshow(x_start[np.random.randint(counts[i]), :, :, :]) 
    plt.title(signlabels[i+1])
    plt.axis('off')

plt.show()

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!

With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.

There are various aspects to consider when thinking about this problem:

  • Neural network architecture (is the network over or underfitting?)
  • Play around preprocessing techniques (normalization, rgb to grayscale, etc)
  • Number of examples per label (some have more than others).
  • Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Pre-process the Data Set (normalization, grayscale, etc.)

Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128 is a quick way to approximately normalize the data and can be used in this project.

Other pre-processing steps are optional. You can try different techniques to see if it improves performance.

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.

In [6]:
# Define a random transformation functions
import cv2 as cv


def img_rand_rotate(img):
    rows,cols,depth = img.shape
    max_angle = 30

    angle_range = np.random.uniform(-1 * max_angle, max_angle)
    M = cv.getRotationMatrix2D((cols/2,rows/2),angle_range,1)

    dst = cv.warpAffine(img, M, (cols, rows))
    return dst

def img_rand_perspective(img):
    rows,cols,depth = img.shape
    
    xshift = np.random.randint(0,8)
    yshift = np.random.randint(0,8)
    
    pts1 = np.float32([[xshift,yshift],[rows-xshift,yshift],[0,cols],[rows,cols]])
    pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])
    
    M = cv.getPerspectiveTransform(pts1,pts2)
    dst = cv.warpPerspective(img,M,(rows,cols))
    
    return dst

def img_rand_shift(img):
    rows,cols,depth = img.shape
    
    xshift = np.random.randint(0,8)
    yshift = np.random.randint(0,8)
    
    M = np.float32([[1,0,xshift],[0,1,yshift]])
    dst = cv.warpAffine(img,M,(cols,rows))
    
    return dst

def img_rand_blur(img):
    blursize = np.random.randint(2,5)
    return cv.blur(img,(blursize,blursize))

def img_rand_trans(img):
    procedure_no = np.random.randint(1,5)
    if procedure_no == 1 :
        return img_rand_rotate(img)
    elif procedure_no == 2 :
        return img_rand_perspective (img)
    elif procedure_no == 3 :
        return img_rand_shift(img)
    elif procedure_no == 4 :
        return img_rand_blur(img)
        
In [7]:
#test rotation

norm_out = np.zeros((32,32,3))

out_img = X_train[1010].copy()

out_img = img_rand_rotate(out_img)

plt.imshow(out_img)

norm_out= cv.normalize(out_img, norm_out, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)

plt.imshow(norm_out)
Out[7]:
<matplotlib.image.AxesImage at 0x7fdaef6870f0>
In [8]:
#test perspective

norm_out = np.zeros((32,32,3))

out_img = X_train[1010].copy()

out_img = img_rand_perspective(out_img)

plt.imshow(out_img)
Out[8]:
<matplotlib.image.AxesImage at 0x7fdaeddd7630>
In [9]:
#test shift


norm_out = np.zeros((32,32,3))

out_img = X_train[1010].copy()

out_img = img_rand_shift(out_img)

plt.imshow(out_img)
Out[9]:
<matplotlib.image.AxesImage at 0x7fdaedd38438>
In [10]:
#test randon process


norm_out = np.zeros((32,32,3))

out_img = X_train[1010].copy()

out_img = img_rand_trans(out_img)

plt.imshow(out_img)
Out[10]:
<matplotlib.image.AxesImage at 0x7fdaedd13e48>
In [11]:
# Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include 
### converting to grayscale, etc.
### Feel free to use as many code cells as needed.

import cv2 as cv
import numpy as np

def process_img(inputimg,outputimg):
    
    return cv.normalize(inputimg, outputimg, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)

X_trainnormalized = []
X_testnormalized = []
X_validnormalized = []

for i in range(0,n_train):   
    outimg = np.zeros((32,32,3))
    outimg = process_img(X_train[i],outimg)
    X_trainnormalized.append(outimg)
    
X_trainnormalized = np.asarray(X_trainnormalized)

#normalize test and validation sets
for i in range(0,n_test):
    outimg = np.zeros((32,32,3))
    outimg = process_img(X_test[i],outimg)
    X_testnormalized.append(outimg)

X_testnormalized = np.asarray(X_testnormalized)
    
for i in range(0,n_validation):
    outimg = np.zeros((32,32,3))
    outimg = process_img(X_valid[i],outimg)
    X_validnormalized.append(outimg)
X_validnormalized = np.asarray(X_validnormalized)
    
plt.imshow(X_trainnormalized[1])
plt.show()
plt.imshow(X_train[1])
plt.show()

    
In [12]:
#display normalized images


plt.figure(figsize=(15, 30))
for i in range(0, n_classes):
    plt.subplot(15, 3, i+1)

    x_selected = X_trainnormalized[y_train == i]
    plt.imshow(x_selected[3, :, :, :]) 
    plt.title(signlabels[i+1])
    plt.axis('off')

plt.show()
In [13]:
# make samples equal

min_trainsetcount = 2000
max_img_add = 200
add_x = []
add_y = []

unq, unq_inv, unq_cnt = np.unique(y_train, return_inverse=True, return_counts=True)
class_index = np.split(np.argsort(unq_inv), np.cumsum(unq_cnt[:-1]))

for cls in unq:
    if unq_cnt[cls] < min_trainsetcount:
        #minimum additional image count
        min_add_imagecount = min_trainsetcount - unq_cnt[cls]
        
        #add some random amount
        new_img_count = np.random.randint(min_add_imagecount,min_add_imagecount+max_img_add)
        
        for i in range(new_img_count):
            rand_i = np.random.choice(class_index[cls])
            out_img = img_rand_trans(X_trainnormalized[rand_i])
            
            add_x.append(out_img)
            add_y.append(y_train[rand_i])
            
X_trainnormalized = np.vstack((X_trainnormalized, np.asarray(add_x)))
y_train = np.hstack((y_train, np.array(add_y)))
In [14]:
classes, counts = np.unique(y_train, return_counts=True)

fig = plt.figure(figsize=(10,10))

plt.barh(classes,counts)
#plt.yticks(counts, signlabels)
plt.ylabel('Classes')
plt.xlabel('Frequency')
plt.title('Histogram of the classes')

plt.show()
print(counts)
    
[2057 2095 2010 2142 2058 2140 2028 2026 2070 2167 2002 2155 2085 2107 2075
 2056 2006 2031 2002 2093 2081 2123 2093 2038 2185 2073 2025 2097 2146 2098
 2096 2124 2033 2080 2008 2105 2020 2068 2176 2017 2043 2022 2150]

Model Architecture

In [15]:
import tensorflow as tf

EPOCHS = 35
BATCH_SIZE = 128
In [16]:
from tensorflow.contrib.layers import flatten

def LeNet(x):    
    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
    mu = 0
    sigma = 0.1
    
    # SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 3, 6), mean = mu, stddev = sigma))
    conv1_b = tf.Variable(tf.zeros(6))
    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID',name="conv1") + conv1_b

    # SOLUTION: Activation.
    conv1 = tf.nn.relu(conv1)

    # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID',name="conv1max")

    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
    conv2_b = tf.Variable(tf.zeros(16))
    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID',name='conv2') + conv2_b
    
    # SOLUTION: Activation.
    conv2 = tf.nn.relu(conv2)

    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID',name="conv2max")

    # SOLUTION: Flatten. Input = 5x5x16. Output = 400.
    fc0   = flatten(conv2)
    
    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
    fc1_b = tf.Variable(tf.zeros(120))
    fc1   = tf.matmul(fc0, fc1_W) + fc1_b
    
    # SOLUTION: Activation.
    fc1    = tf.nn.relu(fc1)
    # add drop_out
    fc1  = tf.nn.dropout(fc1, keep_prob)
    
    # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
    fc2_b  = tf.Variable(tf.zeros(84))
    fc2    = tf.matmul(fc1, fc2_W) + fc2_b
    
    # SOLUTION: Activation.
    fc2    = tf.nn.relu(fc2)
    # add drop_out
    fc2    = tf.nn.dropout(fc2, keep_prob)
    

    # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43.
    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
    fc3_b  = tf.Variable(tf.zeros(43))
    logits = tf.matmul(fc2, fc3_W) + fc3_b
    
    return logits

Train, Validate and Test the Model

A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.

In [17]:
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected, 
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.

x = tf.placeholder(tf.float32, (None, 32, 32, 3))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 43)
keep_prob = tf.placeholder(tf.float32) 
In [18]:
rate = 0.001

logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
In [19]:
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    sess = tf.get_default_session()
    for offset in range(0, num_examples, BATCH_SIZE):
        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y,keep_prob: 1.0})
        total_accuracy += (accuracy * len(batch_x))
    return total_accuracy / num_examples
In [20]:
from sklearn.utils import shuffle

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    num_examples = len(X_trainnormalized)
    
    print("Training...")
    print()
    for i in range(EPOCHS):
        X_trainnormalized, y_train = shuffle(X_trainnormalized, y_train)
        for offset in range(0, num_examples, BATCH_SIZE):
            end = offset + BATCH_SIZE
            batch_x, batch_y = X_trainnormalized[offset:end], y_train[offset:end]
            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.6})
            
        validation_accuracy = evaluate(X_validnormalized, y_valid)
        print("EPOCH {} ...".format(i+1))
        print("Validation Accuracy = {:.3f}".format(validation_accuracy))
        print()
        
    saver.save(sess, './lenet')
    print("Model saved")
Training...

EPOCH 1 ...
Validation Accuracy = 0.745

EPOCH 2 ...
Validation Accuracy = 0.847

EPOCH 3 ...
Validation Accuracy = 0.878

EPOCH 4 ...
Validation Accuracy = 0.916

EPOCH 5 ...
Validation Accuracy = 0.920

EPOCH 6 ...
Validation Accuracy = 0.928

EPOCH 7 ...
Validation Accuracy = 0.931

EPOCH 8 ...
Validation Accuracy = 0.932

EPOCH 9 ...
Validation Accuracy = 0.933

EPOCH 10 ...
Validation Accuracy = 0.929

EPOCH 11 ...
Validation Accuracy = 0.935

EPOCH 12 ...
Validation Accuracy = 0.936

EPOCH 13 ...
Validation Accuracy = 0.943

EPOCH 14 ...
Validation Accuracy = 0.941

EPOCH 15 ...
Validation Accuracy = 0.947

EPOCH 16 ...
Validation Accuracy = 0.953

EPOCH 17 ...
Validation Accuracy = 0.948

EPOCH 18 ...
Validation Accuracy = 0.949

EPOCH 19 ...
Validation Accuracy = 0.952

EPOCH 20 ...
Validation Accuracy = 0.939

EPOCH 21 ...
Validation Accuracy = 0.949

EPOCH 22 ...
Validation Accuracy = 0.958

EPOCH 23 ...
Validation Accuracy = 0.958

EPOCH 24 ...
Validation Accuracy = 0.951

EPOCH 25 ...
Validation Accuracy = 0.959

EPOCH 26 ...
Validation Accuracy = 0.947

EPOCH 27 ...
Validation Accuracy = 0.959

EPOCH 28 ...
Validation Accuracy = 0.960

EPOCH 29 ...
Validation Accuracy = 0.960

EPOCH 30 ...
Validation Accuracy = 0.960

EPOCH 31 ...
Validation Accuracy = 0.959

EPOCH 32 ...
Validation Accuracy = 0.957

EPOCH 33 ...
Validation Accuracy = 0.963

EPOCH 34 ...
Validation Accuracy = 0.959

EPOCH 35 ...
Validation Accuracy = 0.963

Model saved
In [21]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    test_accuracy = evaluate(X_testnormalized, y_test)
    print("Test Accuracy = {:.3f}".format(test_accuracy))
INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.942

Step 3: Test a Model on New Images

To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.

You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

Load and Output the Images

In [22]:
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
# german sign data online test data

import os
import glob
test_classid = []

with open('./test/GTSRB/Online-Test/Images/GT-online_test.csv', 'r') as csvfile:
    readCSV = csv.reader(csvfile, delimiter=';')
    for row in readCSV:
        test_classid.append(row[7])


image_list = []
y_selftest = []

i=0
for filename in sorted(glob.glob('./test/GTSRB/Online-Test/Images/0000*.ppm')):
    im = np.zeros((32,32,3))
    print(filename)
    im = process_img(cv.resize(cv.cvtColor(cv.imread(filename,cv.IMREAD_UNCHANGED), cv.COLOR_BGR2RGB),(32,32)),im)
    image_list.append(im)
    i += 1
    y_selftest.append(int(test_classid[i]))

num_images = len(image_list)

y_selftest = np.asarray(y_selftest)
plt.figure(figsize=(15, 30))

for i in range(0, num_images):
    plt.subplot(15, 3, i+1)
    plt.title(signlabels[y_selftest[i] + 1])
    plt.imshow(image_list[i]) 
    plt.axis('off')

plt.show()

image_list = np.asarray(image_list)
./test/GTSRB/Online-Test/Images/00000.ppm
./test/GTSRB/Online-Test/Images/00001.ppm
./test/GTSRB/Online-Test/Images/00002.ppm
./test/GTSRB/Online-Test/Images/00003.ppm
./test/GTSRB/Online-Test/Images/00004.ppm
./test/GTSRB/Online-Test/Images/00005.ppm
./test/GTSRB/Online-Test/Images/00006.ppm
./test/GTSRB/Online-Test/Images/00007.ppm
./test/GTSRB/Online-Test/Images/00008.ppm
./test/GTSRB/Online-Test/Images/00009.ppm
In [23]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    test_accuracy = evaluate(image_list, y_selftest)
    print("Test Accuracy = {:.3f}".format(test_accuracy))
INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 1.000
In [24]:
test2_classid = []
image2_list = []
y2_selftest = []


with open('./test_internet/sings.csv', 'r') as csvfile:
    readCSV = csv.reader(csvfile, delimiter=';')
    for row in readCSV:
        test2_classid.append(row[1])        
i=0

for filename in sorted(glob.glob('./test_internet/*.png')):
    im = np.zeros((32,32,3))
    print(filename)
    
    # since the most of the images are blured blur increases slightly 
    im = process_img(cv.resize(cv.cvtColor(cv.imread(filename,cv.IMREAD_COLOR), cv.COLOR_BGR2RGB),(32,32)),im)
    image2_list.append(im)
    y2_selftest.append(int(test2_classid[i]))
    i += 1

num_images = len(image2_list)

y2_selftest = np.asarray(y2_selftest)
plt.figure(figsize=(15, 30))

for i in range(0, num_images):
    plt.subplot(15, 3, i+1)
    plt.title(signlabels[y2_selftest[i] + 1])
    plt.imshow(image2_list[i]) 
    plt.axis('off')

plt.show()

image2_list = np.asarray(image2_list)
./test_internet/01.png
./test_internet/02.png
./test_internet/03.png
./test_internet/04.png
./test_internet/05.png
./test_internet/06.png
./test_internet/07.png
./test_internet/08.png
./test_internet/09.png
./test_internet/10.png
./test_internet/11.png
./test_internet/12.png
./test_internet/13.png
./test_internet/14.png
./test_internet/15.png
./test_internet/16.png
./test_internet/17.png
./test_internet/18.png
In [25]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    test_accuracy = evaluate(image2_list, y2_selftest)
    print("Test Accuracy = {:.3f}".format(test_accuracy))
INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.778

Predict the Sign Type for Each Image

In [26]:
import tensorflow as tf

### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.
prediction = tf.argmax( logits, 1 )

# Redeclare in case you want to run this cell alone.
saver = tf.train.Saver()

with tf.Session() as sess:
    
    saver.restore(sess, './lenet')
    output = sess.run(prediction, feed_dict={
        x: image2_list, 
        keep_prob: 1.0})
INFO:tensorflow:Restoring parameters from ./lenet

Analyze Performance

In [27]:
### Calculate the accuracy for these 5 new images. 
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.


correct = 0
wrong = 0

for i in range(len(y2_selftest)):
    if output[i] == y2_selftest[i]:
        correct +=1
    else:
        print(i)
        wrong +=1

print("correct matches : ",correct)
print("wrong matches : ",wrong)

print("correct rate : ", correct / len(y2_selftest) )
0
12
14
16
correct matches :  14
wrong matches :  4
correct rate :  0.7777777777777778

Output Top 5 Softmax Probabilities For Each Image Found on the Web

For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k could prove helpful here.

The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.

tf.nn.top_k will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.

Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tf.nn.top_k is used to choose the three classes with the highest probability:

# (5, 6) array
a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,
         0.12789202],
       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,
         0.15899337],
       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,
         0.23892179],
       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,
         0.16505091],
       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,
         0.09155967]])

Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3)) produces:

TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],
       [ 0.28086119,  0.27569815,  0.18063401],
       [ 0.26076848,  0.23892179,  0.23664738],
       [ 0.29198961,  0.26234032,  0.16505091],
       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],
       [0, 1, 4],
       [0, 5, 1],
       [1, 3, 5],
       [1, 4, 3]], dtype=int32))

Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202], you can confirm these are the 3 largest probabilities in a. You'll also notice [3, 0, 5] are the corresponding indices.

In [28]:
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. 
### Feel free to use as many code cells as needed.




softmax = tf.nn.softmax( logits)

# Redeclare in case you want to run this cell alone.
saver = tf.train.Saver()

with tf.Session() as sess:
    
    saver.restore(sess, './lenet')
    outputall = sess.run(softmax, feed_dict={
        x: image2_list, 
        keep_prob: 1.0})
    top_val,top_i = sess.run(tf.nn.top_k(outputall, k=5))
    
for i in range(0,len(y2_selftest)):
    print("-------------------------------------------------")
    print("Correct image : ", signlabels[y2_selftest[i] + 1])
    for j in range(0,5):
        print( signlabels[top_i[i][j] + 1] + ": %0.4f" % top_val[i][j] )
    print("-------------------------------------------------")
INFO:tensorflow:Restoring parameters from ./lenet
-------------------------------------------------
Correct image :  Roundabout mandatory
Turn right ahead: 0.9546
Roundabout mandatory: 0.0254
Keep left: 0.0194
End of speed limit (80km/h): 0.0004
Go straight or left: 0.0002
-------------------------------------------------
-------------------------------------------------
Correct image :  Stop
Stop: 0.9970
Bicycles crossing: 0.0015
Yield: 0.0010
Speed limit (30km/h): 0.0003
No vehicles: 0.0001
-------------------------------------------------
-------------------------------------------------
Correct image :  Road work
Road work: 1.0000
Bicycles crossing: 0.0000
Double curve: 0.0000
Wild animals crossing: 0.0000
Beware of ice/snow: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Priority road
Priority road: 1.0000
Traffic signals: 0.0000
Yield: 0.0000
No vehicles: 0.0000
Right-of-way at the next intersection: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  General caution
General caution: 1.0000
Traffic signals: 0.0000
Pedestrians: 0.0000
Road work: 0.0000
Right-of-way at the next intersection: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Speed limit (20km/h)
Speed limit (20km/h): 1.0000
Speed limit (30km/h): 0.0000
Speed limit (120km/h): 0.0000
Speed limit (70km/h): 0.0000
No vehicles: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Speed limit (60km/h)
Speed limit (60km/h): 0.8522
Speed limit (50km/h): 0.1452
Speed limit (80km/h): 0.0026
No passing: 0.0000
No passing for vehicles over 3.5 metric tons: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Keep right
Keep right: 1.0000
Go straight or right: 0.0000
Turn left ahead: 0.0000
Keep left: 0.0000
Ahead only: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Go straight or left
Go straight or left: 1.0000
Ahead only: 0.0000
Roundabout mandatory: 0.0000
Road work: 0.0000
Turn right ahead: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  No passing
No passing: 0.9999
Vehicles over 3.5 metric tons prohibited: 0.0001
Speed limit (60km/h): 0.0000
Speed limit (20km/h): 0.0000
Children crossing: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Dangerous curve to the left
Dangerous curve to the left: 0.9879
Slippery road: 0.0121
Wild animals crossing: 0.0000
Double curve: 0.0000
Bicycles crossing: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Yield
Yield: 1.0000
Speed limit (30km/h): 0.0000
Priority road: 0.0000
No vehicles: 0.0000
Keep left: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Speed limit (30km/h)
Speed limit (20km/h): 0.8353
Speed limit (60km/h): 0.0737
Speed limit (30km/h): 0.0477
Vehicles over 3.5 metric tons prohibited: 0.0248
Speed limit (80km/h): 0.0073
-------------------------------------------------
-------------------------------------------------
Correct image :  Go straight or right
Go straight or right: 1.0000
Turn left ahead: 0.0000
Ahead only: 0.0000
Turn right ahead: 0.0000
Keep right: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Speed limit (50km/h)
Speed limit (30km/h): 0.6806
Roundabout mandatory: 0.1826
Right-of-way at the next intersection: 0.0508
Speed limit (70km/h): 0.0241
Pedestrians: 0.0186
-------------------------------------------------
-------------------------------------------------
Correct image :  No entry
No entry: 1.0000
Stop: 0.0000
Vehicles over 3.5 metric tons prohibited: 0.0000
No vehicles: 0.0000
Yield: 0.0000
-------------------------------------------------
-------------------------------------------------
Correct image :  Speed limit (100km/h)
Speed limit (120km/h): 0.9774
Keep left: 0.0063
Speed limit (20km/h): 0.0035
Speed limit (80km/h): 0.0030
Turn right ahead: 0.0030
-------------------------------------------------
-------------------------------------------------
Correct image :  End of all speed and passing limits
End of all speed and passing limits: 0.9954
End of speed limit (80km/h): 0.0044
End of no passing: 0.0002
End of no passing by vehicles over 3.5 metric tons: 0.0000
Priority road: 0.0000
-------------------------------------------------

Project Writeup

Once you have completed the code implementation, document your results in a project writeup using this template as a guide. The writeup can be in a markdown or pdf file.

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.


Step 4 (Optional): Visualize the Neural Network's State with Test Images

This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.

Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the LeNet lab's feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.

For an example of what feature map outputs look like, check out NVIDIA's results in their paper End-to-End Deep Learning for Self-Driving Cars in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.

Combined Image

Your output should look something like this (above)

In [56]:
### Visualize your network's feature maps here.
### Feel free to use as many code cells as needed.

# image_input: the test image being fed into the network to produce the feature maps
# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output
# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry

def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
    # Here make sure to preprocess your image_input in a way your network expects
    # with size, normalization, ect if needed
    # image_input =
    # Note: x should be the same name as your network's tensorflow data placeholder variable
    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function
    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})
    featuremaps = activation.shape[3]
    plt.figure(plt_num, figsize=(15,15))
    for featuremap in range(featuremaps):
        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column
        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number
        if activation_min != -1 & activation_max != -1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray")
        elif activation_max != -1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray")
        elif activation_min !=-1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray")
        else:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray")
In [57]:
saver = tf.train.Saver()

print("---")

with tf.Session() as sess:
    saver.restore(sess, './lenet')
    conv1 = sess.graph.get_tensor_by_name('conv1:0')
    outputFeatureMap(image2_list,conv1)
 
---
INFO:tensorflow:Restoring parameters from ./lenet
In [58]:
saver = tf.train.Saver()

print("---")

with tf.Session() as sess:
    saver.restore(sess, './lenet')
    conv2 = sess.graph.get_tensor_by_name('conv2:0')
    outputFeatureMap(image2_list,conv2)
 
---
INFO:tensorflow:Restoring parameters from ./lenet